1. Data assimilation - the collaborative research centre SFB1294
  2. Open Science
  3. Publications

Publications

  • Gottwald, G., Li, F., Marzouk, Y., Reich, S (2024). Stable generative modelling using diffusion maps. arXiv 2401.04372

  • König, J., Pfeffer, M. and Stoll, M. (2023). Efficient training of Gaussian processes with tensor product structure. arXiv 2312.15305.

  • Engbert, R. and Rabe, M. M. (2023). Tutorial on dynamical modeling of eye movements in reading. PsyArXiv

  • Boys, B., Girolami, M., Pidstrigach, J., Reich, S., Mosca, A., and Akyildiz, O.D. (2023). Tweedie Moment Projected Diffusions For Inverse Problems arXiv 2310.06721

  • Reich, S. (2023): A particle-based Algorithm for Stochastic Optimal Control. arXiv 2311.06906

  • Chen, Y, Huang D.Z., Huang J., Reich, S., and Stuart, A.M. (2023). Sampling via gradient flows in the space of probability measures arXiv:2310.03597

  • Pidstrigach, J., Marzouk, Y., Reich, S., and Wang, S. (2023). Infinite-Dimensional Diffusion Models arXiv 2302.10130

  • Liu, S., Reich, S., and Tong, X.T. (2023). Dropout ensemble Kalman inversion for high dimensional inverse problems arXiv:2308.16784

  • Pasemann, G. and Beta, C. and Stannat, W. (2023). Stochastic Reaction-Diffusion Systems in Biophysics: Towards a Toolbox for Quantitative Model Evaluation. arXiv:2307.06655

  • Gaudlitz, S. (2023). Non-parametric estimation of the reaction term in semi-linear SPDEs with spatial ergodicity.arXiv:2307.05457

  • G. Blanchard, A. Carpentier, and O. Zadorozhnyi (2023): Moment inequalities for sums of weakly dependent random fields. In: arXiv preprint arXiv:2306.16403

  • Kim, J. W. and Mehta, P. G. (2023): Variance Decay Property for Filter Stability. arXiv 2305.12850

  • Chen, Y, Huang D.Z., Huang J., Reich, S., and Stuart, A.M. (2023). Gradient flows for sampling: Mean-field models, Gaussian approximations and affine invariance arXiv:2302.11024

  • Cvetkovic, N. and Lie, H. C. and Bansal, H. and Veroy-Grepl, K. (2023). Choosing observation operators to mitigate model error in Bayesian inverse problems. ArXiv 2301.04863

  • Kim, J.W. and Reich, S. (2023): On forward-backward SDE approaches to continuousßtime minimum variance estimation. arXiv 2304.12727

  • Pidstrigach, J., Marzouk, Y., Reich, S., and Wang., S. (2023). Infinite-dimensional diffusion models for function spaces arXiv:2302.10130

  • Irwin, B. and Reich, S. (2023). EnKSGD: A class of preconditioned black box optimization and inversion algorithms. arXiv:2303.16494.

  • Mach, T and Freitag, M.A. (2023). Solving the Parametric Eigenvalue Problem by Taylor Series and Chebyshev Expansion arXiv 230212.03661

  • Schwetlick, L. and Reich S. and Engbert R. (2023). Bayesian Dynamical Modeling of Fixational Eye Movements. arXiv:2303.11941.

  • Rabe, M. M., Paape, D., Mertzen, D., Vasishth, S., & Engbert, R. (2023). SEAM: An integrated activation-coupled model of sentence processing and eye movements in reading. arXiv:2303.05221

  • Janák, J. and Reiß, M. (2023). Parameter estimation for the stochastic heat equation with multiplicative noise from local measurements. arXiv:2303.00074v1

  • Kemeth, F., Alonso, S., Echebarria, B., Moldenhawer, T., Beta, C. and Kevrekidis I. (2022). Black and Gray Box Learning of Amplitude Equations: Application to Phase Field Systems. arXiv: 2207.03954

  • Schindler, D., Moldenhawer, T., Beta, C., Huisinga, W. and Holschneider, M. (2022). Three-component contour dynamics model to simulate and analyze amoeboid cell motility. arXiv:2210.12978

  • Kim, J. W. and Mehta, P. G. (2022): Duality for Nonlinear Filtering II: Optimal Control. arXiv 2208.06587

  • Kim, J. W. and Mehta, P. G. (2022): Duality for Nonlinear Filtering I: Observability. arXiv 2208.06586

  • König, J. and Freitag, M. (2022). Time-limited Balanced Truncation for Data Assimilation Problems. arXiv 2212.07719.

  • Lie, H. C. and Rudolf, D. and Sprungk, B. and Sullivan, T. J. (2022). Dimension-independent Markov chain Monte Carlo on the sphere. ArXiv 2112.12185

  • Reich, S. (2022): Data assimilation: A dynamic homotopy-based coupling approach. arXiv 2209.05279

  • Calvello, E., Reich, S. and Stuart A.M.(2022): Ensemble Kalman methods: A mean field approach. arXiv 2209.11371

  • Huang, D.Z., Huang, J., Reich, S., and Stuart, A.M. (2022). Efficient derivative-free Bayesian inference for large-scale inverse problems. arXiv:2204.04386.

  • Yadav, H., Smith, G., Reich, S., and Vasishth, S. (2022). Number feature distortion modulates cue-based retrieval in reading. doi:10.31234/osf.io/s4c9t.

  • Gaudlitz, S. and Reiß, M. (2022). Estimation for the reaction term in semi-linear SPDEs under small diffusivity. arXiv:2203.10527

  • Reich, S. (2022). Frequentist perspective on robust parameter estimation using the ensemble Kalman filter arXiv:2201.000611

  • Pidstrigach, J. and Reich, S. (2021). Affine-invariant ensemble transform methods for logistic regression arXiv: 2104.08061

  • Dietrich, F., Makeev, A., Kevrekidis, G., Evangelou, N., Bertalan, T., Reich, S., and Kevrekidis, I.G. (2021). Learning effective stochastic differential equations from microscopic simulations: combining stochastic numerics and deep learning. arXiv:2106.09004

  • Cialenco, I. and Kim, H.-J. and Pasemann, G. (2021). Statistical analysis of discretely sampled semilinear SPDEs: a power variation approach. arXiv:2103.04211

  • Zadorozhnyi, O. and Gaillard, P. and Gerchinovitz, S. and Rudi, A. (2021). Online nonparametric regression with Sobolev kernels. arxiv: 2102.03594

  • Coghi, M., Torstein, N., Nuesken, N., and Reich, S. (2022). Rough McKean-Vlasov dynamics for robust ensemble Kalman filtering arXiv:2107.06621

  • Ba, Y., de Wiljes, J., Oliver, D.S., and Reich, S. (2021). Randomized maximum likelihood based posterior sampling.  arXiv:2101.03612

  • Lange, T. (2020): Derivation of Ensemble Kalman-Bucy Filters with unbounded nonlinear coefficients. arXiv 2012.07572

  • Pathiraja, S. (2020): L2 convergence of smooth approximations of Stochastic Differential Equations with unbounded coefficients. arXiv 2011.13009

  • Castillo, A. M., de Wiljes, J., Shprits, Y. Y., and Aseev, N. A. (2020). Reconstructing the dynamics of the outerelectron radiation belt by means of the standard and ensemble Kalman filter with the VERB-3Dcode, ESSOAr. doi:10.1002/essoar.10504674.

  • Seelig, S., Risse, S., and Engbert, R. (2020). Predictive modeling of the influence of parafoveal informationprocessing on eye guidance in reading.  doi:10.31234/osf.io/vbmqn

  • Holschneider, M., Ferrat, K., Zöller, G., Molkenthin, C., and Hainzl, S. (2020). Richter b-value maps from local moments of seismicity.  arXiv:2010.12298

  • Altmeyer, R. and Bretschneider, T. and Janák, J. and Reiß, M. (2020). Parameter Estimation in an SPDE Model for Cell Repolarisation. arXiv 2010.06340

  • Houdebert, P., Zass, A. (2020), An explicit continuum Dobrushin uniqueness criterion for Gibbs point processes with non-negative pair potentials. arxiv 2009.06352

  • Molkenthin, C., Donner, C., Reich, S., Zöller, G., Hainzl, S., Holschneider, M. and Opper, M. (2020). GP-ETAS: Semiparametric Bayesian inference for the spatio-temporal Epidemic Type Aftershock Sequence model. arXiv:2005.12857

  • Rastogi, A. and Mathé, P. (2020). Inverse learning in Hilbert scales.arXiv 2002.10208

  • Celisse, A. and Wahl, M. (2020). Analyzing the discrepancy principle for kernelized spectral filter learning algorithms.arXiv: 2004.08436

  • Maier C., Hartung N., Kloft C., Huisinga W., de Wiljes J. (2020): Combining reinforcement learning with data assimilation for individualised dosing policies in oncology. arXiv:2006.01061

  • Altmeyer, R. and Cialenco, I. and Pasemann, G. (2020). Parameter estimation for semilinear SPDEs from local measurements. arXiv 2004.14728

  • Zhelavskaya, I., Aseev, N. A., Shprits, Y. Y., and Spasojevi, M. (2020). A combined neural network- and physics-based approach for modeling the plasmasphere dynamics, ESSOAr. doi:10.1002/essoar.10502691.1

  • Duval, C. and Mariucci, E. (2020). Non-asymptotic control of the cumulative distribution function of Lévy processes. arXiv 2003.09281

  • Vernade, C., Carpentier, A., Lattimore, T., Zappella, G., Ermis, B. and Brueckner, M. (2020). Linear Bandits with Stochastic Delayed Feedback. arXiv:1807.02089

  • Spokoiny, V. (2019). Bayesian inference for nonlinear inverse problems. arXiv:1912.12694

  • Lange, T. and Stannat, W. (2019): On the continuous time limit of Ensemble Square Root Filters. arXiv 1910.12493

  • Spokoiny, V., and Panov, M. (2019). Accuracy of Gaussian approximation in nonparametric Bernstein–vonMises theorem. arXiv:1910.06028

  • Nuesken, N. and Reich, S. (2019). Note on Interacting Langevin diffusions: Gradient structure and ensemble Kalman sampler by Garbuno-Inigo, Hoffmann, Li and Stuart. arXiv:1908.10890

  • Houdebert, P. (2019). Phase transition of the non-symmetric Continuum Potts model. arXiv: 1908.10066

  • Avanesov, V. (2019). How to gamble with non-stationary X-armed bandits and have no regrets. arXiv:1908.07636

  • Avanesov, V. (2019). Structural break analysis in high-dimensional covariance structure. arXiv: 1803.00508

  • Avanesov, V. (2019). Nonparametric Change Point Detection in Regression. arXiv:1903.02603

  • Lefakis, L., Zadorozhnyi, O. and Blanchard, G. (2019). Efficient Regularized Piecewise-Linear Regression Trees. arXiv: 1907.00275​​​​​​​ 

  • Zadorozhnyi, O., Blanchard, G. and Carpentier, A. (2019). Restless dependent bandits with fading memory. arXiv: 1906.10454 

  • Blanchard, G., Mathé, P. and Mücke, N. (2019). Lepskii Principle in Supervised Learning. arXiv: 1905.10764 

  • Wahl, M. (2019). A note on the prediction error of principal component regression.arXiv: 1811.02998

  • Carpentier, A., Duval, C. and Mariucci, E. (2019). Total variation distance for discretely observed Lévy processes: a Gaussian approximation of the small jumps. arXiv: 1810.02998

  • Duval, C. and Mariucci, E. (2019). Compound Poisson approximation to estimate the Lévy density. arXiv: 1702.08787 

  • Jirak, M. and Wahl, M. (2018). Perturbation bounds for eigenspaces under a relative gap condition.arXiv: 1803.03868

  • Pathiraja, S. and van Leeuwen, P.J. (2018). Model uncertainty estimation in data assimilation for multi-scale systems with partially observed resolved variables, Quarterly Journal of the Royal Meteorological Society, under review, arXiv: 1807.09621

  • Jirak, M. and Wahl, M. (2018). Relative perturbation bounds with applications to empirical covariance operators.arXiv: 1802.02869

  • Gribonval, R., Blanchard, G.,  Keriven, N. and Traonmilin, Y. (2017). Compressive Statistical Learning with Random Feature Moments.arXiv 1706.07180